Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition (2307.14535v2)

Abstract: We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a LLM to guide high-level planning, and sampling-based robot planners (e.g. motion or grasp samplers) for generating diverse and rich manipulation trajectories. To robustify this data-collection process, the LLM also infers a code-snippet for the success condition of each task, simultaneously enabling the data-collection process to detect failure and retry as well as the automatic labeling of trajectories with success/failure. For (2), we extend the diffusion policy single-task behavior-cloning approach to multi-task settings with language conditioning. Finally, we propose a new multi-task benchmark with 18 tasks across five domains to test long-horizon behavior, common-sense reasoning, tool-use, and intuitive physics. We find that our distilled policy successfully learned the robust retrying behavior in its data collection procedure, while improving absolute success rates by 33.2% on average across five domains. Code, data, and additional qualitative results are available on https://www.cs.columbia.edu/~huy/scalingup/.

Analysis of "Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition"

This paper introduces a novel framework for robot skill acquisition that leverages LLMs and sampling-based planners to efficiently generate and distill data into a robust multi-task, language-conditioned visuo-motor policy. The methodology is structured into two main phases: scaling up data generation and distilling the data into actionable policies.

Framework Overview

The framework is designed around solving the dual challenges of increasing the scale of language-labeled robot data generation and distilling this into effective control policies. Initially, high-level task instructions from a LLM are used in combination with sampling-based planners to generate diverse manipulation trajectories. This includes using tools such as motion and grasp samplers to explore the task space effectively. During this phase, the LLM also infers task-specific success conditions that allow it to automatically label data as successful or unsuccessful, enabling a robust mechanism to retry tasks when failures are detected.

In the data distillation phase, the authors extend the diffusion policy designed for single-task scenarios to multi-task, language-conditioned contexts. The outcome is a closed-loop language-conditioned visuo-motor policy that is capable of execution in real-world environments. The policy is evaluated against an 18-task benchmark across five domains, focusing on the policy's ability to handle long-horizon, common-sense, tool-use, and intuitive physics tasks.

Key Contributions and Results

1. Data Generation Innovation: The integration of LLMs with 6DoF exploration primitives allows for scalable data generation with limited human intervention. The data collected is rich with language and success labels, organized through a hierarchical task decomposition—a notable advantage over baselines that struggle with scalable human-centric data annotation.
2. Robust Policy Distillation: By employing a multi-task language-conditioned diffusion policy, the system benefits from robustness and capability beyond traditional MLP-based approaches. The method yielded a significant average success rate improvement of 33.2% across five domains compared to initial baselines.
3. Evaluation Metrics: Success rates were reported across numerous complex manipulation tasks, showing significant performance improvement over existing methods that rely on less sophisticated data acquisition and policy learning techniques. Results demonstrated the efficacy of the hierarchical planning and retry mechanisms by effectively encapsulating longer task sequences and recovering from failures.

Implications for Robotics and AI

The framework offers a promising direction for leveraging the state-of-the-art in LLMs to enhance robotic skill acquisition. It reflects the potential of integrating LLM-driven planning capabilities with sampling-based robotics to overcome the traditional limitations posed by human-based data generation and expert demonstrations. Additionally, with its ability to automatically handle 6DoF tasks and articulate multi-level plans based on simple task descriptions, this framework could be particularly beneficial in dynamically changing real-world applications, such as home assistance robots or automated industrial systems.

Future Directions

The implications of this research are broad and suggest several possible avenues for future exploration. Notably, the potential scaling of this methodology with advancements in 3D generative modeling could further automate and diversify the task generation phase. Additionally, exploring how language-conditioned policies can adapt through continuous learning in real-world unstructured environments remains an open challenge worth addressing to ensure robustness against a broader set of unforeseen real-world variations.

In conclusion, the paper outlines a comprehensive system for scaling up data collection and distilling it into effective control policies using LLMs, setting a foundation for future advancements in autonomous robotic learning. This integrated framework supplies the robotics field with a method to efficiently bridge the gap between language instructions and practical robotic actions, laying the groundwork for innovations in how robots understand and interact with their environments.